Method And Apparatus For Monitoring Of A Human Or Animal Subject

ABSTRACT

A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.

FIELD

The present invention relates to a method and apparatus for monitoring ahuman or animal subject, and in particular a subject within an enclosedarea, e.g. a room such as a secure room.

CROSS REFERENCE TO RELATED APPLICATIONS

This application discloses similar subject matter to the two co-pendingUS applications of the same date and in the same name which are herebyincorporated by reference, and is related to United Kingdom patentapplication numbers 1900032.2, 1900033.0 and 1900034.8 all filed 2 Jan.2019 and the contents of which are also hereby incorporated byreference.

BACKGROUND

There are many situations where a subject is in an enclosed environment,such as a room in a hospital, secure room in a prison or hospital, oreven a home environment, where a duty of care is placed on an authorityresponsible for the subject. To comply with such duty of carerequirements, it is conventional to monitor subjects in suchenvironments. Such monitoring may comprise regular, scheduled visualchecks by a member of staff and/or continuous video monitoring of thesubject in the room. While such monitoring can be effective,difficulties can arise with the subject's condition changing quicklybetween scheduled checks, or with a lack of movement of the subjectbeing misinterpreted. For example, a subject who is lying still on a bedor on the floor may be resting or asleep, or may have suffered adeterioration in health. Subjects who are under the influence of alcoholor drugs or suffering from a mental condition may behave in ways whichare abnormal and difficult for staff observing them to interpretcorrectly. This increases the burden on staff as subjects must bechecked individually, in person. It may also be necessary or useful torecord the pattern of movement of a subject, e.g. how often they get outof bed at night, their speed, etc., as this can give an indication oftheir physical or mental state, but recording such activity manually islabour intensive.

It would therefore be useful to have a way of automatically monitoringthe subject which provides an indication of their condition, e.g. stateof health or activity level, and that can both record this and alertstaff to those subjects needing attention.

Automatic monitoring of vital signs offers the possibility of mitigatingsome of these problems, but traditional contact-based vital signssensors are restrictive and inconvenient, and some subjects may notco-operate with their use. Recent developments demonstrating that vitalsigns such as heart rate or breathing rate can be detected in videoimages of the human body, where the video images are obtained using astandard video camera, are of significant interest. For exampleVerkruysse et al., “Remote plethysmographic imaging using ambientlight”, Optics Express, 16 (26), 22 Dec. 2008, PP. 21434-21445demonstrated that changes in reflectivity or transmittivity of thesubject's skin caused by cardiac-synchronous variations in the volume ofoxygenated blood in the skin capillaries, known as photoplethysmographicimage or PPGi signals, could be detected in the video signal from aconventional consumer standard video camera where a human subject wasilluminated under ambient light. This idea has been developed furtherin, for example, WO-A2-2013/027027, WO-A-2011/021128 andWO-A1-2015/049150 which aim to increase the reliability of the detectionof the remote PPG signal.

The paper “Distance PPG: robust non-contact vital signs monitoring usinga camera” by Mayank Kumar et al.; 6 Apr. 2015; Biomedical Optics Express1565, 1 May 2015, Vol. 6 No. 5, discusses a method of combiningskin-colour change signals from different tracked regions of a subject'sface using a weighted average, where the weights depend on the bloodperfusion and incident light density in the region to improve thesignal-to-noise ratio of the camera-based estimate. It discusses thevarious challenges for camera-based non-contact vital sign monitoringand proposes that improvements in the signal-to-noise ratio of thecamera-based estimates reduces the errors in vital sign estimation.

Many of the prior art techniques have been based on careful control ofboth the subject being monitored and the lighting conditions in theenvironment. Thus, although they claim success in detecting the heartrate or vital signs of the subject, in general the subjects wererequired to remain relatively still, the subjects were not obscured andthe lighting conditions were kept relatively constant. These conditionsare in general not true of real situations where lighting changes, e.g.with sunlight through a window, the subject moves or may becomeobscured, there may be confounding sources of movement such as otherpeople, insects or equipment such as fans in the room, or movement fromoutside the room but which is visible through a window or that affectsthe lighting in the room.

Other techniques based on detecting fine movement associated withbreathing or heart activity from a combination of movement andmicro-blushes (PPGi) have also been proposed. In the health and securitymonitoring fields proposals have also been made for detecting andclassifying the gross movement of subjects in a video image as dangerousor non-dangerous, for example the proposal for detecting clonic seizuresas described in the paper “Real-time automated detection of clonicseizures in newborns” by Pisani et al.

Another common problem with such video image analysis is finding andtracking the subject in the video image. The human body is naturallydeformable and the orientation of the subject with respect to thecamera's view point can vary significantly. Also the subjects may bestill, in which case motion-based detection and tracking can fail, ormay move significantly or in unpredictable ways, which can be difficultfor feature-based techniques. Even in a relatively simple visual scene,such as a single human subject in a fairly plain room (as may be foundin care or secure institutions such as hospitals, care homes, detentioncentres or prisons), subjects may be covered with bedclothes, which canmake them difficult to detect automatically, and actions such asthrowing bedclothes across the room can cause image features which werepreviously associated with the subject to move across the image and thusconfuse automatic analysis. Subjects mix periods of high activity andlarge movement with periods of relative immobility (seated or lying),will in general be clothed and have bedding to cover themselves. Thus,periods of inactivity while lying down, may coincide with the subjectcovering themselves partly or completely with bedding. Further,illumination may vary between daylight and artificial light and securerooms are sometimes lit with visible artificial light and are sometimescompletely dark, with infrared being the only illumination available.Other sources of regular or irregular movement may also appear in thescene being monitored—e.g. insects flying in, ventilation fans, domesticappliances.

Also, the arrangement of the video monitoring apparatus itself may causedifficulty for the video analysis. For safety reasons the video cameraor cameras have to be positioned out of reach of the subject, normallyhigh in a corner of the room. This means that the view of the subjecttends to be compressed by perspective and the subject is only arelatively small fraction of the field of view. Further, because themonitoring has to continue in the dark (when the subject is asleep), itis normal to use a monochrome infrared camera, which means thattechniques relying on full colour images do not work.

In the context of monitoring the health and welfare of subjects for whoman institution may have a duty of care, the reliability of the system inreal conditions is paramount, otherwise the system cannot be relied uponas helping discharge the institution's duty of care.

Existing systems do not provide monitoring, including vital signsmonitoring such as heart or breathing rate detection, which operatesreliably in the face of these difficulties associated with the widevariety of poorly-controlled settings in which such monitoring may beused.

Similar problems of movement and variable illumination occur also inother fields such as fitness and health and well-being in the home, on afarm, in a zoo or elsewhere.

Being able to monitor a subject in these less controlled conditions andprovide practically useful information would significantly improve theability to monitor the well-being of such a subject and to comply withduty of care requirements, particularly in the health or security field.As with all monitoring systems, the primary need is to avoid excessivefalse alarming and also to avoid excessive under alarming. Excessivefalse alarming leads to monitoring systems being ignored by staff, orswitched off. Excessive under alarming leads to a lack of trust in thesystem and does not meet the basic requirements of the monitoringsystem.

One aspect of the present invention therefore provides a method ofmonitoring a human or animal subject comprising the steps of: capturinga video image of the subject consisting of a time series of image frameseach frame comprising a pixel array of image intensity values; analysingthe video image to detect signals comprising temporal variations in theimage intensity representative of movement of the subject; andoutputting an indication of the movement of the subject; wherein thestep of analysing the video image to detect signals comprising temporalvariations in the image intensity representative of movement of thesubject comprises: measuring the variation with time of the imageintensity at each of a plurality of positions in the image to form arespective plurality of movement signals; grouping the movement signalsinto a plurality of groups according to their position in the image,quantifying the variability in each of the movement signals and formingfor each group a representative single movement signal, determiningwhether the variability of the representative movement signal is above apredetermined threshold, and determining movement as being present atthat position in the image if the variability of the retained movementsignal is above the predetermined threshold.

The step of measuring the variation with time of the image intensity ateach of a plurality of positions in the image may comprise detectingspatial intensity variations representing edges in the image to form anedge image; and measuring the variation with time of the edge image ateach of a plurality of positions in the image to form the respectiveplurality of movement signals.

The step of detecting spatial intensity variations representing edges inthe image to form an edge image may comprise applying a kernelconvolution to each frame of the image, the kernel convolution combiningthe intensities of a plurality of neighbouring pixels in the frame todetect edges in the image.

The method may further comprise the step of applying a function to theimage intensities that magnifies lower intensities and reduces theprominence of greater intensities to produce a non-linearly-scaledimage, and the step of measuring the amount of variation in imageintensity with time is conducted upon on the resultant image. An exampleis to determine the logarithm of the image intensities in each frame toform a logarithm image and wherein said step of analysing is conductedupon the logarithm image.

The step of forming for each group a representative single movementsignal may comprise retaining for each group a predetermined ordinal oneof the movement signals ordered by their variability, e.g. the fifth tothe fifteenth largest, e.g. the tenth largest, determining whether theamplitude variability of the retained movement signals are above apredetermined threshold, and determining movement as being present atthat position in the image if the variability of the retained movementsignal is above the predetermined threshold.

Each of said plurality of positions may comprise at least one pixel ofthe image. Optionally the intensity values of a plurality ofneighbouring pixels are combined together, e.g. by averaging or taking arepresentative one, to form a spatially resized image upon which saidanalysis step is conducted.

A temporally resized image upon which said analysis step is conductedmay be formed by combining together corresponding pixel values in aplurality of successive frames of the video image.

Predetermined areas of said video image may be masked out. The methodmay further comprise the step of detecting global intensity variationsin the image and de-correlating them from the image. The step ofdetecting global intensity variations may comprise detecting variationsin image intensity in predefined areas of the image. The step ofdetecting global intensity variations may comprise detecting principalcomponents in the variations in image intensity, and retaining asrepresentative of global intensity variations only those principalcomponents whose variability is above a predetermined threshold.

Another aspect of the invention, which may be combined with any of theabove aspects, provides a method of monitoring a human or animal subjectcomprising the steps of: capturing a video image of the subjectconsisting of a time series of image frames each frame comprising apixel array of image intensity values; analysing the video image todetermine automatically one or more regions of interest in the image inwhich variations in the image intensity contain signals representativeof the physiological state of the subject, said signals comprising atleast one vital sign and subject movement; analysing the intensityvalues in the regions of interest to determine subject movement and atleast one vital sign of the subject; wherein the step of determiningautomatically one or more regions of interest in the image comprisesanalysing the image to measure the amount of variation in imageintensity with time at each of a plurality of positions in the image,and selecting as regions of interest those positions at which the amountof variation in image intensity with time is above a predeterminedthreshold.

Each of said plurality of positions may comprise at least one pixel ofthe image, e.g. a plurality of neighbouring but not necessarilycontiguous pixels in each frame, or pixels from nearby or in a localarea of the image, whose intensity values are combined together, forexample by averaging.

The number of neighbouring pixels whose intensity values are combinedtogether may be set to a first value for determining subject movementand to a second value, different from said first value, for determiningsaid at least one vital sign. The second value may be set in dependenceupon the vital sign being determined.

The video image may be temporally resized before analysis by combiningtogether a plurality of successive frames of the video image. The numberof frames that are combined together may be set to a first value ifsubject movement is being determined and to a second value, differentfrom said first value, if a respiration rate of said subject is beingdetermined.

The spatial and/or temporal resizing are advantageous if the dataprocessing burden needs to be reduced.

Predetermined areas of said video image may be masked out. Such areasmay be defined, e.g. on set-up, by an operator and may correspond toimage areas that are expected to include confounding sources of imageintensity variation or movement.

In one embodiment a function may be applied to the image intensitiesthat magnifies lower intensities and reduces the prominence of greaterintensities to produce a non-linearly-scaled image, and the step ofmeasuring the amount of variation in image intensity with time isconducted upon on the resultant image. One example of this is to takethe logarithm of the image intensities to form a logarithm image, andimage analysis is conducted upon the logarithm image. Other scalingfunctions such as taking the square root of the intensities may be used.

The step of analysing the image to measure the amount of variation inimage intensity with time at each of a plurality of positions in theimage may comprise analysing the video image to automatically determinethe amount of movement by detecting spatial intensity variationsrepresenting edges in the image to form an edge image; and measuring thevariation with time of the edge image at each of a plurality ofpositions in the image to form a respective plurality of movementsignals. An edge image is one where edges in the image are enhanced,i.e. have greater intensity. One way of achieving this is to calculatethe derivative (first or a higher derivative) of the image, e.g. byapplying a kernel convolution.

The method may further comprise grouping the movement signals into aplurality of groups according to their position in the image,quantifying the variability in each of the movement signals and formingfor each group a representative single movement signal, determiningwhether the variability of the representative movement signal is above apredetermined threshold, and determining movement as being present atthat position in the image if the variability of the retained movementsignal is above the predetermined threshold.

The step of detecting spatial intensity variations representing edges inthe image to form an edge image may comprise applying a kernelconvolution to each frame of the image, the kernel convolution combiningthe intensities of a plurality of neighbouring pixels in the frame todetect edges in the image.

The method may further comprise the step of detecting global intensityvariations in the image and de-correlating the measured amount ofvariation in image intensity with time from the detected globalintensity variations. The step of detecting global intensity variationsmay comprise detecting variations in image intensity in predefined areasof the image, e.g. by detecting principal components in the variationsin image intensity, and retaining as representative of global intensityvariations only those principal components whose variability is above apredetermined threshold.

Another aspect of the invention, which may be combined with any of theabove aspects, provides a method of monitoring a human or animal subjectcomprising the steps of: capturing a video image of the subjectconsisting of a time series of image frames each frame comprising apixel array of image intensity values; and analysing the video image todetermine automatically one or more vital signs of the subject; whereinthe step of determining automatically one or more vital signs of thesubject comprises: analysing the image to detect a plurality of signalseach comprising the variation in image intensity with time at each of arespective plurality of positions in the image, determining a firstplurality of signal quality indices of the signals and retaining onlythose signals whose signal quality indices are above a predeterminedthreshold, analysing the retained signals in a multi-dimensionalcomponent analysis to obtain components thereof and retaining apredetermined number of the strongest components, determining a secondplurality of signal quality indices of the retained components,selecting amongst the retained components on the basis of the secondplurality of signal quality indices, determining the frequency of theselected components, and outputting a vital sign estimate based on saiddetermined frequencies.

Further stages which thin out the number of signals for the downstreamprocessing steps, i.e. of analysing signal quality indices and retainingonly those whose signal quality indices are above a predeterminedthreshold, may be added before or after the multi-dimensional componentanalysis.

Each of said plurality of signals may comprise the variation inintensity at a plurality of pixels in a local neighbourhood in each ofsaid image frames whose intensity values are combined together to formone of said plurality of signals The pixels in the local neighbourhoodmay be pixels that are adjacent, or within a predetermined distance.

Said plurality of positions in the image may be positions in the imageat which subject movement has been detected.

The method may further comprise the step of frequency filtering each ofsaid plurality of signals to exclude those outside predeterminedexpected or allowed physiological range for said one or more vitalsigns.

The method may further comprise the step of scaling intensities in theimage as mentioned above, e.g. by calculating the logarithm of saidimage intensity values to form a logarithm image, or using some otherscaling function, and said step of analysing the image is performed onthe resulting image.

Said first plurality of signal quality indices may comprise measures ofone or more of periodicity, such as peak consistency and amplitudeconsistency. These indices are relatively quick to calculate and do notrequire large processing resources.

The step of analysing each of the retained signals to find thecomponents thereof may comprise a multi-dimensional component analysisto decompose the signals into their components, examples being principalcomponents analysis, ICA (Independent Component Analysis).

The second plurality of signal quality indices may comprise measures ofone or more of: peak consistency, amplitude consistency, location in theimage, distance between signals in the image, and variability. Themethod may further comprise the step of determining from said secondplurality of signal quality indices a confidence value for each of saidretained components and using said confidence value in said selectingstep. Thus more signal quality indices, potentially including ones whichrequire more processing resources are carried out on a significantlyreduced number of signals, increasing the efficiency of the method.

The selecting step may comprise weighting said retained components bysaid confidence value and updating a prior estimate of said one or morevital signs by said weighted components. The method may further comprisethe step of down-weighting said prior estimate of said one or more vitalsigns by a predetermined amount before updating it with said weightedcomponents. Thus a running estimate of vital signs is maintained, basedon previous estimates and their confidence, and the current estimates,with the influence of previous estimates decaying with time.

The method may further comprise the step of detecting the amount ofsubject movement in the image and varying said predetermined amount,e.g. in dependence upon the detected amount of subject movement. Thusthe influence of previous estimates on the running estimate may decaymore quickly in some circumstances. One example is where a lot ofmovement is detected in the image.

The step of determining from said second plurality of signal qualityindices a confidence value for each of said retained components mayconducted by a machine learning technique such as a trained artificialneural network. Other ways of calculating a confidence value are alsopossible, such as logistic regression, a non-linear combination of thecomponents and their SQIs, or other machine learning techniques.

Another aspect of the invention, which may be combined with any of theabove aspects, provides a method of monitoring a human or animal subjectcomprising the steps of: capturing a video image of the subjectconsisting of a time series of image frames each frame comprising apixel array of image intensity values; analysing the video image todetermine automatically at least one of subject movement or at least onevital sign of the subject by analysing the image to detect a pluralityof signals each comprising the variation in image intensity with time ateach of a respective plurality of positions in the image; detectingglobal intensity variations in the image and de-correlating the detectedglobal intensity variations from said plurality of signals; wherein thestep of detecting global intensity variations comprises detectingvariations in image intensity in predefined areas of the image.

The image intensity may be spatially averaged in each of the predefinedareas of the image.

The step of detecting global intensity variations may comprise detectingcomponents in the variations in image intensity, and retaining asrepresentative of global intensity variations only those componentswhose variability is above a predetermined threshold.

The method may further comprise the step, after de-correlating forglobal intensity variations, of rescaling the plurality of signals inaccordance with the number of retained components. This may be achievedby dividing each pixel value in the time window by a number based on thenumber of retained components.

The components are determined by principal components analysis oranother special analysis technique.

Each of said plurality of positions may comprise at least one pixel ofthe image. For example, each of said plurality of positions comprises aplurality of neighbouring pixels whose intensity values are combinedtogether, e.g. by averaging or taking a representative one.

The number of neighbouring pixels whose intensity values are combinedtogether may be set to a first value for determining subject movementand to a second value, different from said first value, for determiningsaid at least one vital sign. The second value may be set in dependenceupon the vital sign being determined.

The video image may be temporally resized before the analysing steps bycombining together a plurality of successive frames of the video image.The number of frames that are combined together may be set to a firstvalue if subject movement is being determined and to a second value,different from said first value, if a respiration rate of said subjectis being determined.

Predetermined areas of said video image may be masked out.

The logarithm of the image intensities may be taken to form a logarithmimage, and the step of analysing the image to detect said plurality ofsignals may be conducted upon on the logarithm image.

The method may further comprise detecting spatial intensity variationsrepresenting edges in the image to form a derivative image; measuringthe variation with time of the derivative image at each of a pluralityof positions in the image to form a respective plurality of movementsignals.

The methods of the different aspects of the invention may furthercomprise the step of compensating the image for pixel noise bydown-weighting signals having low image intensity. The method mayfurther comprise applying a saturation mask to mask out image areaswhose image intensity is above a predetermined maximum brightness orbelow a predetermined minimum brightness.

One or more of the above aspects of the invention and optional orpreferred features may be combined together.

The video camera may be a standard digital video camera so that thevideo image sequence is a conventional frame sequence with each framecomprising a spatial array of pixels of varying image intensities and/orcolours. The camera may be monochrome or may be a colour cameraproviding pixel intensities in the red, green and blue channels.

The video image sequence may be time-windowed, i.e. divided into batchesof successive frames for processing, and the analysis steps areconducted on successive time windows, with an output for each timewindow. The time windows may be of different length depending on thetarget of the analysis, e.g. different time windows for detectingmovement, breathing rate or heart rate. For example windows of 120, 180or 600 frames, corresponding to 6, 9 or 30 seconds at 20 frames persecond, may be used respectively for movement, heart rate and breathingrate analysis. Successive time windows may be overlapping, for exampleby 1 second, resulting in an output each second, that output being basedon the frames forming the time window.

The invention may also be embodied in a computer program for processinga captured video image sequence in accordance with the invention and foroutputting the results on a display. Such a computer program may run ona general purpose computer of conventional type or on a dedicated videoprocessor.

DRAWINGS

The invention will be further described by way of non-limitative examplewith reference to the accompanying drawings in which:—

FIG. 1 schematically illustrates a room containing a subject undermonitoring in accordance with an embodiment of the invention;

FIG. 2 is a flow diagram of the overall processing in one embodiment ofthe invention;

FIG. 3 is a flow diagram of the system set-up steps in one embodiment ofthe invention;

FIG. 4 schematically illustrates an example image frame of a video imagein one embodiment of the invention;

FIG. 5 is a flow diagram of the video signal acquisition and analysis inone embodiment of the invention;

FIG. 6 is a flow diagram of the video signal pre-processing in oneembodiment of the invention;

FIG. 7 is a flow diagram of part of the video signal processing in oneembodiment of the invention;

FIG. 8 is a flow diagram of part of the video signal processing in oneembodiment of the invention;

FIG. 9 is a flow diagram of part of the video signal processing to forma saturation mask in one embodiment of the invention;

FIG. 10 is a flow diagram of part of the video signal processing todetect movement and interpret it in one embodiment of the invention;

FIG. 11 is a flow diagram of part of the video signal processing forestimation of heart rate and breathing rate in one embodiment of theinvention;

FIG. 12 is a flow diagram of part of the video signal processing forestimation of heart rate and breathing rate in one embodiment of theinvention;

FIG. 13 schematically illustrates a report obtainable with oneembodiment of the invention.

DETAILED DESCRIPTION

FIG. 1 schematically illustrates an apparatus in accordance with anembodiment of the invention being used to monitor a subject 3 in a room1 a. The room 1 a can be a secure room such as a cell in a policestation or prison or some other detention facility, or could be a roomin a hospital or other care facility such as a care home, shelteredaccommodation, the subject's own home or an environment used to shelteranimals. It may be one of plural rooms being monitored as indicated bythe neighbouring room 1 b. The subject 3 is monitored by a video camera5 a whose output is processed by a video signal processor 7 and theresults of the analysis are displayed on a display 9 which is visible tostaff of the facility. The video signal processor 7 receives inputs fromthe video cameras 5 b in other rooms. The video signal processor 7 maybe a dedicated signal processor or a programmed general purposecomputer. The rooms may be naturally lit or may be artificiallyilluminated using a visible light source 11 or infrared light source 13and include a door 2 and may include one or more windows 4. Furniture,such as a bed 6, may be present.

The video camera 5 a (b) is a standard digital video camera outputtingvideo data in the form of a sequence of image frames, each frame being apixel array of intensities in either monochrome or red, green, bluechannels. The red, green and blue channels also give a response in theinfrared range allowing the production of an infra-red (IR) image usefulwhen the room is dark. Video cameras of this type typically output thesignal at thirty frames per second, though of course different framerates are possible as long as the rate is greater than the minimumrequired to estimate the highest frequencies in the signal beinganalysed.

The display 9 preferably displays the video image of the rooms and alsodisplays information regarding the physiological state, e.g. health orsafety, of the subject 3. This information is preferably one or moreof:—

-   -   Whether movement is present.    -   Whether vital signs are being acquired.    -   Whether the subject is judged to be safe.    -   Current values of estimated vital signs such as heart rate and        breathing rate.    -   Whether no vital signs have been detected and the time for which        no vital signs have been detected.    -   A no movement and no vital signs alert or alarm.

Staff monitoring the subject by way of the display 9 can therefore tellat any given time whether the subject can be considered safe, forexample because they are moving or because the vital signs are beingdetected and are in a physiologically normal range, or whether thesystem is unable to detect vital signs but safe movement is detected(and for how long that situation has persisted), or that no vital signsand no movement is present, in which case an alert is generatedinstructing staff to check the subject. If the lack of vital signsdetection persists for more than a configurable amount of time an audioand/or visual alert may be generated to call on staff to check thesubject. Alerts can included a range of electronic notification methodsincluding, but not limited to, automated telephone message, pager, SMS,as well as indication on the display 9 with the alert containing thecondition and location of the subject and the condition being alerted.

As well as providing live monitoring information the system may alsorecord and provide a summary report of the status of the subject 3, suchas activity level by time through the day, vital signs and any alertsraised during predetermined periods, e.g. daily, weekly, monthly, and/orfor the complete period the subject is in the room.

FIG. 2 is a flow diagram showing the overall processing by the videoprocessor 7 in one embodiment of the invention. Firstly, in step 200 thesystem is set up by an operator. This involves not only physicallypositioning or checking the cameras 5 a, 5 b, but also viewing theimages obtained by the video cameras and defining certain regions whichwill be used by the video signal processing as will be described later.Such regions may include, for example, background regions which areparts of the image that are not expected to include an image of thesubject 3, areas to be masked-out such as images of undesired featuressuch as the window 4, and specific areas where movement is of particularinterest because they are expected to be occupied by the subject 3. Suchareas may be on or next to furniture such as bed 6, areas near the door2, or areas near to other internal doors (for example in a care home, asubject's room may adjoin a bathroom and it may be of interest to detectmovement of the subject through a region next to the door of thebathroom). In the case of application to animal habitats such areas maybe sleeping areas or feeding or drinking areas of an animal enclosure.The subsequent normal operation of the system is then generallyillustrated by steps 201-204. In step 201 the video image from thecamera or cameras 5 a, b is acquired by the video signal processor 7 andin step 202 the video image is analysed by the video signal processor 7to provide status outputs relating to the physiological state of thesubject 3. The steps of acquiring and analysing the video image continuewhile the system is in operation. The status outputs from the analysisof the video images are interpreted by the video signal processor 7 instep 203 and displayed on display 9 in step 204. Steps 202 to 204 willbe described in more detail below.

FIGS. 3 and 4 illustrate in more detail one example of the set-up step200 of FIG. 2. In step 300 the video cameras 5 a, b are set up andconnected to the video signal processor 7, or an existing set-up andconnection is checked, and video images of the rooms are viewed on thedisplay 9. In step 301, for each of the rooms 1 a, 1 b, regions 7 in theimage are defined which correspond to background regions where thesubject 3 is not expected to be present. These will be used later in thedetection of global intensity variations, such as changes in sunlight orartificial light, which are unrelated to the subject's condition.Examples including regions on the ceiling or high on the walls.Typically at least three such background regions are defined.

In step 302 areas 8 of the image are defined which should be masked-outand excluded from analysis. These are typically areas which may includea source of movement or lighting change which is not related to theactivity of the subject. An example would be the window 4, where thecamera may be able to see movements from outside the room, or lightingchanges from outside. Other examples would be sources of confoundingmovement in the room such as curtains, mirrors, hatches connecting toother rooms, domestic appliances (e.g. ventilation fans).

In step 303 certain areas 9 of the image are defined where subjectmovement is of particular interest. These areas may include an areaaround the door 2, an area next to the bed 6, an area on the bed 6.Where the room includes interconnecting doors, further areas 9 may bedefined near them, and further areas 9 may be defined on and near otheritems of furniture such as desks, chairs, tables etc.

FIG. 5 breaks down into more detail steps 202 to 204 of FIG. 2 relatingto the processing of the video signal. The same general form of processflow is used by the video signal processor 7 to analyse the video signalto detect three targets—subject movement, subject's breathing rate andsubject's heart rate, but certain parameters in the process, e.g. thelength of the time window analysed and certain filtering parameters,differ between the three targets. In the following description, forconciseness, the process flow will be described once, noting where thereare differences for the three targets of the analysis, it beingunderstood that the video signal processor is in practice running thethree analyses in parallel.

In step 501 the video image from the camera or cameras 5 a, b isreceived by the video signal processor 7. In step 502 the video signalprocessor 7 then takes a time window of F frames of the video signal. Aswill be discussed in more detail below, the length of the time window,i.e. the number of frames F taken for processing, depends on whether theanalysis is seeking to detect and quantify movement of the subject, todetect a heart rate of the subject or to detect a breathing rate of thesubject. Successive overlapping time windows will be defined and each isprocessed separately. The overlap may be, for example, from 0.5 to 5seconds, more preferably 0.9 to 1.1 seconds, e.g. 1 second.

In step 503 the image is pre-processed as discussed in more detailbelow, for example to compensate for varying shutter times of the cameraand to spatially and temporally resize the image. Other pre-processingsteps may be present, such as applying Gaussian blurring. In step 504 anoise image representing the amount of expected pixel noise iscalculated and, in parallel, the logarithm of the intensity at eachpixel position in the resized image is taken to produce a sequence oflogarithm images.

In step 505 the sequence of logarithm images is processed to detectareas of the image where most subject movement appears to be occurring.These areas constitute regions of interest for subsequent analysis. Anaim of this embodiment is to find such regions of interest automaticallyand quickly, so that they can be found automatically in real timewithout requiring further operator input after the initial set-up.

Having located the regions of interest and compensated for noise and theeffects of global intensity variations such as changes in sunlight, instep 506 the subject movement, heart rate and breathing rate areobtained from the regions of interest as will be explained in moredetail below. The subject movement is assessed as being present or notpresent in each of the areas 9 that were predefined in the system set-upand heart rate and breathing rate estimates are obtained by componentanalysis of the image signals in the regions of interest identified instep 505. In step 507 the movement determination and vital signestimates are interpreted automatically to determine the status of thesubject. In step 508 the current status of the subject is output and theheart rate and breathing rate estimates are also output and these aredisplayed on display 9. The status of the subject and the heart rate andbreathing rate estimates may be recorded in an activity log in step 509and the process then returns to process the next overlapping time windowof F frames in step 510.

FIG. 6 illustrates an example of the pre-processing 503 in more detail.In step 600 the video image is received. Typically a video image willconsist of a time series of image frames, each frame comprising a pixelarray of image intensity values. Typically the frame rate may be 30frames per second of frames with a 1600×1600 pixel array of 12 bitintensity values. Each frame will also be associated with a shutter timecorresponding to the image acquisition time and this is typicallyautomatically controlled by the camera to achieve a consistent exposure.

In step 601 a time window of F frames of the video image are taken. Asmentioned above the value of F depends on the target of the imageanalysis. This embodiment of video imaging processing has three targets,namely the detection of subject movement (for example gross movement),estimation of the subject's breathing rate (which may be characterisedas fine movement), and estimation of the subject's heart rate which willbe obtained on the basis of the photoplethysmographic signal in theimage). In this example, to target the subject's movement the timewindow is set to 8 seconds and thus F is eight times the frame rate, totarget heart rate estimation the time window is set to 12 seconds andthus F equals twelve times the frame rate, and for breathing rateestimation the time window is 50 seconds and thus F equals fifty timesthe frame rate. In different embodiments different length time windowsmay be taken for each of these targets and thus the particular value ofF may be varied. For example, to detect movement the time window may beof 5 to 10 seconds, for detecting the heart rate the time window may beof 5 to 15 seconds, and for detecting breathing rate the time window maybe of 20 to 60 seconds. The particular values are not essential to theinvention but the breathing rate time window is generally selected to belonger than that for movement detection or heart rate estimation and theexact choice is a compromise between the frequency range to be detected,and the desired speed of reaction of the estimation to changes infrequency.

In the following processing, the original image may be processed byvarious operations on the intensity values, including blurring,averaging, kernel convolution, and rescaling the image to reduce thenumber of pixels. The term image will be used to describe not only theoriginal raw image from the video camera, but also the rescaled array ofprocessed values each associated with a particular position, and the newvalue at each position will still be referred to as a pixel value,though each such processed and rescaled pixel value will have beenderived from several pixels in the original (raw) image and thus berepresentative of a larger area of the original image than the originalpixels. The variation with time of the intensity at a particularposition, i.e. the temporal variation of each pixel value through thetime window, whether original/raw or processed and rescaled, can betaken together to form a time varying signal, which will have one sampleper frame and a length equal to the duration of the time window. Thus ifeach frame is of x,y pixels, the time window would produce x×y suchsignals.

In step 602, in order to reduce subsequent processing burdens, each ofthe image frames is spatially resized by a factor depending on thetarget of analysis, by averaging together neighbouring pixel values inthe frame. Where the target of analysis is movement detection thespatial resizing is by a factor of 0.3, where the target of the analysisis heart rate estimation the spatial resizing is by a factor of 0.1 andwhere the target of the analysis is breathing rate estimation, thespatial resizing is by a factor of 0.14. Thus the time windowed videoimage is now a set of F frames each of a reduced number x′, y′ ofpixels. The factor by which the image frames are resized may be varieddepending on the downstream processing capability. Thus more of areduction may be required if the processing capability is lower, but atthe expense of accuracy.

In step 603, in the case only of analysing the video image to estimateheart rate, a Gaussian blur is applied. This involves applying a kernelconvolution with a Gaussian profile. Typical parameters for the Gaussianblur are a kernel size of 5 pixels and sigma 0.8. Gaussian blurring isnot applied in the case of analysing the video image for movement orbreathing rate.

In step 604 the resulting image is compensated for shutter timevariations by dividing each pixel value by the shutter time for thecorresponding frame. Then in step 605 the images are temporally resizedby a factor which, again, is set in accordance with the analysis target,by averaging a number of consecutive frames together. In thisembodiment, for movement detection and breathing rate estimation threeconsecutive frames are averaged into one, and for heart rate estimationtwo adjacent frames are averaged into one. This therefore results in apre-processed set of F′ frames each of x′ by y′ pixels.

The pre-processed video images are then passed to two streams ofprocessing as illustrated in FIG. 7.

In a first stream A, in step 701 the logarithm of each of the pixelvalues in the pre-processed video images are calculated to produce asequence of logarithm images. The logarithm images are subject to asunlight compensation in process 702 (to be explained below) and noisecompensation and edge detection 703 (to be explained below), and theresultant images are combined to produce a movement image in step 704,which represents areas of the original image in which subject movementis likely to be present. This movement image is used for heart rate andbreathing rate estimation in process 705 and for movement calculation instep 706.

In a parallel stream B, a noise image is calculated to indicate for theF′ frames how much of the variation in pixel value at each of the x′ byy′ pixel positions is likely to be caused by pixel noise and how much byother sources. With a typical image sensor, as the image gets brighter,more pixel noise is expected and thus the amount of variability of thepixel value (e.g. the variance) is proportional to both the intensityand the shutter time. Because the analysis for subject movement, heartrate and breathing rate will depend on detecting the variation in pixelvalues, compensation for the pixel noise is advantageous. Thus in step707 the video images are multiplied by their corresponding shutter timesand further temporally resized by averaging all F′ frames of the timewindow together. This results in a single frame of x′ by y′ pixelvalues. Then a kernel convolution is applied in step 708 to pre-adjustfor the fact that an edge kernel convolution will be applied to the mainimage. Thus the noise kernel used in step 708 is the elementwise squareof the edge kernel to be used in the main image analysis describedbelow. The kernel depends on the target of the analysis: for heart rateestimation the edge kernel is the identity matrix (i.e. no convolution),for movement and breathing rate it is the Laplacian of Gaussian (LoG)kernel such as:

0 −1 0 −1 4 −1 0 −1 0

Thus the noise kernel is the identity matrix for heart rate, but formovement detection and breathing rate estimation is:

0 1 0 1 16 1 0 1 0

This convolution provides a noise image which will be used for noisecompensation in calculation of the derivative image 703.

FIG. 8 is a flow diagram illustrating the generation of the movementimage 704 through the application of global intensity variationcompensation 702, spatial filtering 703 and noise compensation 709, tothe sequence of log images 701.

To perform compensation for global intensity variations, such assunlight, in step 800 the R background regions defined at set-up aretaken and in step 801 the pixel values in the logarithm image in each ofthose R regions are averaged for each of the F′ frames. This results inR signals each having F′ sample values (the average intensity in thatbackground region in that frame). Principal components analysis is thenapplied to the R signals in step 802 and in step 803 the standarddeviation of each of the principal components is calculated. Only thoseprincipal components with a standard deviation above a threshold TH1 areretained. This results in r principal components which are expected tobe representative of the main global intensity variations in the image,such as those caused by sunlight. These principal components will beused to de-correlate global intensity variations from the image underanalysis.

Thus in a parallel stream 703, an edge image, in this case a derivativeimage is calculated which, for breathing rate and movement, representsthe presence of edges (i.e. sharp changes in image intensity in theimage frame). To obtain this edge image, in step 805 a convolutionkernel, the Laplacian of Gaussian edge kernel, is applied to thelogarithm image. In the case of movement detection and breathing rateestimation, which will be estimated based on subject movement in theimage, the edge kernel may be:

0 −1 0 −1 4 −1 0 −1 0which is a spatial filter that detects edges in the image. It isequivalent to taking the second spatial derivative of the imageintensity. Areas where the intensity is uniform are reduced to zero, andareas for which the intensity gradient is changing, as at an edge in theimage, are enhanced.

An alternative method for creating the edge image would be to use acombination (usually the Euclidian norm) of horizontal and verticalSobel (or Prewitt) filters applied to the image rather than calculatinga derivative image using the above kernel. Another edge detectiontechnique that can be used is the Canny edge detector, which builds uponthe derivative to find linked edges and presents edges that are morelikely to be true edges from the intensity image.

In the case of heart rate, the heart rate estimation will be estimatedby looking for photoplethysmographic (PPG) signals in the image ratherthan movement. Consequently the edge kernel for heart rate estimation isthe identity matrix.

Then in step 806 areas of the images that were predefined at set-up asunlikely to include subject movement are masked-out.

The resulting sequence of images is used in two ways. Firstly it is usedin the elimination of global intensity variations, and secondly it isused to produce an image representing where subject movement is present.To compensate for global intensity variations, therefore, in step 806the correlation is calculated between each of the r retained principalcomponents of average intensity variations in the R predefinedbackground regions and the sequence of images resulting from step 806.The result is a single frame correlation image of x′ by y′ pixels foreach principal component, in which each pixel value is the correlationbetween the respective one of the r retained principal components andthe variation through the F′ frames at each x′ by y′ pixel position. TheL2 norm is then applied framewise to produce a single correlation imagerepresenting the correlation across all the retained principalcomponents.

The correlation image effectively indicates how much of the intensityvariation in each pixel is likely to be a result of a global intensityvariation.

Returning to the edge images, following masking-out of unwanted regionsin step 806, in step 807 the amount of variability over the F′ frames ofeach of the x′ by y′ pixels is quantified. One example of suchquantification is to calculate the standard deviation over the F′frames. Other examples include the interquartile range in variation, themaximum range, etc. This results in step 808 in a standard deviationimage consisting a single x′ by y′ image frame representing the amountof variation through the whole time window at each x′,y′ pixel position.In step 809 this is multiplied by the square root of the noise imagecalculated in step 709 to compensate for pixel noise. This effectivelyadjusts for the fact that, in a sequence of logged images containing nomovement or illumination changes, the variability of dark pixels wouldinherently be higher than that of a bright pixel. Thus multiplying bythe square root of the noise image effectively down-weights darkerpixels.

In step 810 the noise-compensated standard deviation image isde-correlated against global intensity variations by multiplying by:

√{square root over (1−(correlation image)^(p))}

The power p is chosen between 0.5 and 2.0 and may be set experimentallyby reference to video sequences from a particular set-up. A lower powertends to increase the degree of compensation for sunlight changes. Theresult is a single image of x′ by y′ pixel values which represent theamount of movement-related intensity variation in the video image overthat time window. In step 811 a saturation mask is applied. Thesaturation mask eliminates areas where pixels have recorded an imageintensity above a maximum or below a minimum brightness during theoriginal F frames.

FIG. 9 illustrates an embodiment of constructing a saturation mask. Instep 901 the original F x by y pixel video images are spatially resizedby a factor of 0.5 in both vertical and horizontal directions in theimage by averaging neighbouring pixels together. Then at each resizedpixel position the pixel value is set to zero if the intensity value isgreater than a pre-set maximum or less than a pre-set minimum at anypoint through the F frame time window. The F frames are then temporallyresized over the whole time window by setting the pixel value to zero ifthat pixel is zero at any frame in the time window, or to one otherwise.This results in a single frame of x/2 by y/2 values that are zero orone. The mask is then dilated by at least one pixel by setting any pixelto zero if any of its eight nearest neighbours (Moore neighbourhood) arezero. This then forms a saturation mask which is applied in step 811 tothe image from step 810 by multiplying the pixel values together. Thiseliminates from the image any areas where pixels were above thepredetermined maximum intensity or below the predetermined minimumintensity in the time window.

Finally in step 812 a rescaling operation is applied by dividing eachpixel value by (F′−1−r) where r is the number of retained principalcomponents from step 806. This compensates for a possible variation inthe number of retained principal components used in the global intensityvariation compensation which would otherwise affect the result.

The result of step 812 is a single x′ by y′ frame of values whichindicate how much intensity variation and edge movement exists in thatpixel position in the time window. This can be regarded as a movementimage for the time window and it will be used to quantify the amount ofsubject movement and for estimation of the subject's heart rate andbreathing rate.

FIG. 10 illustrates one embodiment of how the movement image is used toprovide status indications relating to movement of the subject.Recalling that the movement image is a single x′ by y′ frame of pixelvalues corresponding to the amount of variation of that pixel throughthe time window, in step 1001 for each of the predefined regions 9 wheremovement of the subject would be of interest (as set up in step 303), amovement value is selected as representative for the region and comparedto a threshold. In this example the tenth largest pixel value is takenas the movement value, but a different pixel may be used, or somecombination of pixel values, for instance the mean of the 92^(nd) to97^(th) most intense pixel values. This movement value is then comparedto a threshold and if it is above a predetermined threshold thenmovement is regarded as being present for that region and if it is belowthe threshold, movement is regarded as being not present for thatregion. It would be possible to take an ordinal other than the tenthlargest value, for example values between the land 100 will work and theparticular selection of the ordinal value taken is chosen by testing thealgorithm on video images from a particular set up. Alternatively, themovement image can be spatially filtered before taking the nth highestvalue—Gaussian blur, morphological erosion, median blur, allowing thechoice of a smaller value for n. Alternatively, a classifier, such as aconvolutional neural network, may be trained and applied to classifymovement images as either containing or not containing human movement.

Each time window of F frames thus provides a movement present or notpresent indication for each of the predetermined regions 9 in the room.As the process analyses the video image by looking at successiveoverlapping time windows, a next set of movement values for eachpredetermined region 9 in the room will be produced when the processingof the next time window has been completed. In this embodiment the timewindows overlap by one second and thus a new movement indication isoutput for each region 9 every second.

In step 1003 the movement indications for the regions 9 optionallytogether with an indication of whether breathing has been detected, adoor status signal 1004, and the estimates of heart rate and breathingrate are applied to an interpretation module for automaticinterpretation of the subject status. The door status signal is based ona door sensor and indicates whether the door is open or closed and thetime since closing or opening. Alternatively, the status of the door maybe determined by conventional analysis of the video image andrecognising the door as open or closed in the image. In this embodimentthe door status signal has four states: 1) door open; 2) door unknown;3) door recently shut (e.g. 0-29 seconds ago) and door prolongedly shut(eg. >29 seconds).

The interpretation module may be a logic engine using combinatoriallogic to associate predefined subject statuses (e.g. alive, dead,sleeping, presence in different parts of the room, room unoccupied) withparticular inputs of movement estimation, door status, and vital signs.The logic may be fixed, e.g. a tabulated relationship between the outputstatuses and the inputs, or the inputs may be applied to a state machinewhich has a plurality of predefined states corresponding to differentconditions of the subject in the room and is operable to transitionbetween those states in a predefined way based on the input of thesubject movement and breathing rate. Alternatively a machine learningalgorithm or expert system may be trained to output a classified subjectstatuses according to the inputs.

FIGS. 11 and 12 illustrate one embodiment of the processing to estimatethe heart rate and breathing rate of the subject of step 705. The aim inthis embodiment is to use the results of the movement detection above tonarrow down the amount of image to be processed to derive accurate heartand breathing rate estimates. In step 1100 the x′ by y′ movement imagefrom step 704 is taken and used to create a mask eliminating all but thetop N-values where N is chosen according to the processing poweravailable whilst remaining large enough to accurately produce the HR andBR estimates. For example, may be selected as the top 5000 pixels whichis considerably less than the total number of pixels in the ′ by y′frame. This therefore represents the locations in the image frame wheresubject movement is most likely. In step 1102 this movement mask isapplied to each of the F′ frames in the derivative image from step 805.Thus only those values are retained which are in areas of the imagewhere movement is expected. image from step 805. Thus only those valuesare retained which are in areas of the image where movement is expected.

In step 1103 global intensity variations are removed by de-correlatingthe signals against the PCs from 804 as follows: From each signal amultiple of each PC is subtracted, where the multiples are chosen suchthat, after subtraction, the Pearson correlation between the signal andeach PC is zero.

The variations in edge image values value at each of the x′ by y′positions with time through the F′ frames are taken as signals (x′×y′signals of length equal to the product of the frame rate and the timewindow duration). The signals are filtered according to the target ofthe analysis. For heart rate estimation a bandpass filter is applied toeliminate contributions which have a frequency outside the expectedphysiological range for heart rate. For example, a Butterworth bandpassfilter may be used with a pass band of 0.7 to 2.5 Hz (44 to 150 beatsper minute). To estimate breathing rate the signals are subject tolinear de-trending and Gaussian smoothing.

The aim then is to reduce the number of signals by looking at indices ofsignal quality. As each time window will be generating N (5000 in theexample above) signals, it is advantageous if signal quality indiceswhich are quick to calculate are used in this first reduction of thenumber of signals for processing.

Thus, for example, peak consistency, amplitude consistency andwavelength consistency for each of the signals may be calculated toprovide measures of periodicity. An indication of peak consistency is totake all the peak to peak or trough to trough times in each signal andcalculate the standard deviation divided by the mean of those times. Anindication of amplitude consistency is to take the standard deviation ofthe amplitude divided by the mean amplitude for the frequency window. Anindication of wavelength consistency is to take the standard deviationof the times between mean crossings. In all three cases low valuesindicate greater consistency and thus that the signal is of higherquality.

In step 1105 these measures of consistency are multiplied together and anumber of the best signals are taken, for example the top M. In thisexample M=200 but the exact number is not critical. Where more computingpower is available for downstream processing, more signals may be taken.

In step 1106 principal components analysis is performed on these top Msignals and a small number of the top principal components are retained.The number of retained principal components may vary with the target ofthe analysis. Typically between 3 and 10 may be chosen.

In step 1108 the frequencies of the retained principal components arecalculated, these being candidate frequencies for the vital sign (heartrate or breathing rate) which is the target of the analysis. In step1109 an estimate of the vital sign (heart rate or breathing rate) basedon the frequency of the retained principal components is output. Thisestimate may be based on a confidence value calculated for each of theretained principal components in steps 1110 and 1112. This confidencevalue may be based on signal quality indices selected from: peakconsistency, amplitude consistency, wavelength consistency, spatialdispersion (a measure of how the signals are spatially dispersed throughthe area of the image), the standard deviation of the signals, solidity(a measure of whether the signals are from clustered areas of theimage), the uniformity of the signals and the amount of movementassociated with the signal in the area the signal comes from (this is ofinterest because a high level of movement associated with a heart ratesignal, for instance, indicates that the signal may be spurious). Othersignal quality indices may be used in addition to or instead of these,or fewer of these may be used.

Examples of measures of the peak consistency and amplitude consistencywere given above and the same measures may be used here.

A way of calculating a measurement of how each principal component isspatially dispersed through the image is to look at the loadings fromeach signal, i.e. how much of each of the 200 signals in step 1105contribute towards it (this will be a 200 element column matrix). Adistance matrix is constructed of, for example, the Euclidian distancein the image frames between all of the different possible pairs of the200 signals in step 1105. This distance matrix (which will be a 200×200element matrix) may be element-wise multiplied by the outer product ofthe absolute values of the principal component loadings (i.e. all pairsof the loadings multiplied together) to produce a 200×200 matrix ofdistances weighted by loading. Summing all of the values in this matrixgives a single value which is a measure of the spatial dispersion of thesignals that contributed to the principal component. This spatialdispersion measure is, therefore, highest when the strongestcontributing signals are far apart, and lower otherwise. It effectivelymeasures the density of high loadings. This is a useful signal qualityindex because it would be expected that movement associated with asubject in the image would be strong and localised, rather than diffuse.

The spatial dispersion value is obtained for each principal componentretained in step 1107.

The principal component loadings may also be used to calculate a signalquality index indicative of solidity. This is a measure of whether thesignals that contribute to the retained principal components are fromclustered areas of the image which would be expected for an area of skinof the subject. It is therefore a signal quality index which iseffective at recognising a heart rate signal.

To calculate a solidity signal quality index for each retained principalcomponent, absolute values of the loadings are taken together with thecorresponding pixel locations for the two hundred signals of step 1105.The loadings are then placed into an x′ by y′ array (i.e. the same sizeas the image) in the corresponding x′, y′ location. This effectivelycreates a loadings image. A spatial filter is then applied to theloadings image, for example a median filter with a 3×3 kernel size, andthis effectively favours areas which are clustered and have a highloading. Clustered areas where there are five non-zero values next toeach other will retain a value, but areas which are isolated, i.e.surrounded by zeros, will themselves be set to zero. The solidity isthen calculated as the sum of these retained values.

A different approach to calculating solidity would be to apply thedensity-based spatial clustering of applications with noise (DBSCAN)algorithm to the loadings image to determine the clustering of loadings.The solidity would be calculated from the sum of the loadings of valuesthat belonged to clusters with a minimum size.

The size of the kernel in the spatial averaging filter may be varieddepending on the resolution of the video camera and the expected size ofskin areas in the image.

A signal quality index indicative of uniformity of the retainedcomponents may also be calculated from the principal component loadings.The absolute loadings may be subject to L1 normalisation (in which eachvalue is divided by the total of the values) and then the result iscubed. The absolute values of the cubes are summed, and the total isthen subtracted from 1, to give a single value indicative of theuniformity of the signal.

A different measure of uniformity can be generated by taking the inverseof the standard deviation of the loadings. This will be higher forcomponents with less variability in their loadings.

A signal quality index which indicates the amount of movement associatedwith the signal in the area the signal comes from can be calculated byusing the principal component loadings and in particular by thresholdingthe absolute values of the loadings to return a subset of the strongestloadings. This subset is used to determine which pixel locations in themovement image are associated with the signal by extracting the movementvalues from this set of pixel locations and taking a representativevalue, e.g. the median value, as the amount of movement SQI. The amountof movement is then judged as to whether it is appropriate orinappropriate for the signal detected—e.g. a heart rate signal shouldnot be associated with a high level of movement. The method for findingthis SQI can be varied within the same concept. For instance, it isalternatively possible to:

-   -   scale the movement values at each pixel by the associated        loading value before finding the median;    -   find another representative value for the movement amount, e.g.        the mean of the interquartile range of movement values instead        of the median;    -   find the largest contiguous cluster of pixel locations in the        movement image associated with the signal and take a        representative (e.g. the mean) movement value from it;    -   use a movement image derived from dense optical flow, or in some        other way, rather than the specific method detailed above;    -   and any valid combination of the above.

Having calculated a number of signal quality indices, these signalquality indices are then used to select amongst the principal componentfrequencies to output in step 1109 the best estimate of the vital sign(heart rate or breathing rate). One way of achieving this is to use thesignal quality indices to calculate a single confidence value asindicated in step 1112. This can be done by logistic regression or by amachine learning technique trained on a data set for which the actualheart rate and breathing rate are known. For example, an artificialneural network may be trained to output a confidence value between zeroand one based on the signal quality indices for each of the principalcomponents from step 1107.

The confidence value and frequency of each principal component may thenbe input into a normal density filter, to output an estimate of thevital sign as shown in FIG. 12. For example, a histogram may bemaintained with bins respectively corresponding to each possible outputvital sign frequency and each of the frequencies from step 1108 maycontribute to the appropriate histogram bin with a weight determined byits confidence value. The histogram will be updated once for each timewindow with the frequency values from step 1108 weighted by theirconfidence from step 1112. A proportion of each value weighted by theconfidence may also be added to neighbouring bins in a normaldistribution as indicated in step 1203. Preferably each of the existinghistogram counts is decayed by a factor before being updated with thenew values from the current time window as indicated by step 1206. Adecay factor of 20%, for example, may be used for each process cycle(time window).

Whichever histogram bin has the highest count will be regarded as thecurrent estimate of the vital sign, and the corresponding estimate isoutput and displayed in step 1205. The estimate is only output if theheight of the bin exceeds a certain minimum threshold. If no bin exceedsthe minimum threshold then no estimate of the vital sign is output.

The decay of existing values may be adjusted depending on the amount ofmovement detected in the image as indicated by step 1207. Consequentlyif the amount of subject movement is above a predetermined threshold,the decay may be increased, for example to 75%, to reduce the influenceon the estimate of previous values. This also may result in the highesthistogram count not exceeding the minimal threshold for output, whichreflects the fact that when there is high subject movement in the image,estimates of heart rate and breathing rate will be less accurate and sothe algorithm is less able to estimate the vital sign accurately.

As indicated above, the results of the video image analysis in the formof detected subject movement and any estimated vital signs may be outputcontinuously on the display 9. In addition the system may providesummary reports relating to the movement pattern of the subject (basedon detection of movement in the regions 9) and/or their vital signs, forexample over a particular period. FIG. 13 illustrates an example reportfor the night time for a subject in a care home. In this example reporton the left hand side is a colour-coded time bar for the period of nighttime in which periods in bed 140 and out of bed 141 are colour-codeddifferently, and the specific times for being out of bed and gettinginto bed are indicated too. Because of the ability of the process todetect movement at the door and whether or not the door is open, theactivity report also includes a list 142 of room entry times. Summaryindicators of the amount of time in bed 143, the number of times out ofbed 144 and the number of room entries 145 may also be provided. Thevital signs may be indicated at 146. This may be a simple indication ofhow many times vital signs were estimated, or an indication of themeasured vital signs during the period can be given.

The invention may be embodied in a signal processing method, or in asignal processing apparatus which may be constructed as dedicatedhardware or by means of a programmed general purpose computer orprogrammable digital signal processor. The invention also extends to acomputer program for executing the method and to a storage mediumcarrying such a program.

The invention comprises the following embodiments and features two ormore of which may be combined together.

A method of monitoring a human or animal subject comprising the stepsof: capturing a video image of the subject consisting of a time seriesof image frames each frame comprising a pixel array of image intensityvalues; analysing the video image to determine automatically one or moreregions of interest in the image in which variations in the imageintensity contain signals representative of the physiological state ofthe subject, said signals comprising at least one vital sign and subjectmovement; analysing the intensity values in the regions of interest todetermine subject movement and at least one vital sign of the subject;wherein the step of determining automatically one or more regions ofinterest in the image comprises analysing the image to measure theamount of variation in image intensity with time at each of a pluralityof positions in the image, and selecting as regions of interest thosepositions at which the amount of variation in image intensity with timeis above a predetermined threshold.

The method above may have one or more of the following optionalfeatures: Such a method wherein each of said plurality of positionscomprises at least one pixel of the image. Such a method wherein each ofsaid plurality of positions comprises a plurality of neighbouring, butnot necessarily contiguous, pixels whose intensity values are combinedtogether. Such a method wherein the number of neighbouring pixels whoseintensity values are combined together is set to a first value fordetermining subject movement and to a second value, different from saidfirst value, for determining said at least one vital sign. Such a methodwherein the second value is set in dependence upon the vital sign beingdetermined. Such a method wherein the video image is temporally resizedbefore the analysing steps by combining together a plurality ofsuccessive frames of the video image. Such a method wherein the numberof frames that are combined together is set to a first value if subjectmovement is being determined and to a second value, different from saidfirst value, if a respiration rate of said subject is being determined.Such a method wherein predetermined areas of said video image are maskedout before said analysing steps. Such a method wherein a function isapplied to the image intensities that magnifies lower intensities andreduces the prominence of greater intensities to produce anon-linearly-scaled image, and the step of measuring the amount ofvariation in image intensity with time is conducted upon on theresultant image. Such a method wherein the logarithm of the imageintensities is taken to form a logarithm image, and the step ofmeasuring the amount of variation in image intensity with time isconducted upon on the logarithm image. Such a method wherein a functionis applied to form an edge image which has high intensities at edges inthe image, and the step of measuring the amount of variation in imageintensity with time is conducted upon the edge image. Such a methodwherein a kernel convolution is applied to form the edge image bycalculating the derivative, and the step of measuring the amount ofvariation in image intensity with time is conducted upon on thederivative image. Such a method further comprising the step of detectingglobal intensity variations in the image and de-correlating the measuredamount of variation in image intensity with time from the detectedglobal intensity variations. Such a method wherein the step of detectingglobal intensity variations comprises detecting variations in imageintensity in predefined areas of the image. Such a method wherein thestep of detecting global intensity variations comprises detectingprincipal components in the variations in image intensity, and retainingas representative of global intensity variations only those principalcomponents whose variability is above a predetermined threshold. Such amethod further comprising the step of compensating the image for pixelnoise by down-weighting signals having low image intensity.

Another embodiment, which may be used alone or with the embodimentabove, provides a method of monitoring a human or animal subjectcomprising the steps of: capturing a video image of the subjectconsisting of a time series of image frames each frame comprising apixel array of image intensity values; and analysing the video image todetermine automatically one or more vital signs of the subject; whereinthe step of determining automatically one or more vital signs of thesubject comprises: analysing the image to detect a plurality of signalseach comprising the variation in image intensity with time at each of arespective plurality of positions in the image, determining a firstplurality of signal quality indices of the signals and retaining onlythose signals whose signal quality indices are above a predeterminedthreshold, analysing the retained signals in a multi-dimensionalcomponent analysis to obtain components thereof and retaining apredetermined number of the strongest components, determining a secondplurality of signal quality indices of the retained components,selecting amongst the retained components on the basis of the secondplurality of signal quality indices, determining the frequency of theselected components, and outputting a vital sign estimate based on saiddetermined frequencies.

The methods above may have one or more of the following optionalfeatures: Such a method wherein each of said plurality of signalscomprises the variation in intensity at a plurality of pixels in a localneighbourhood in each of said image frames whose intensity values arecombined together to form one of said plurality of signals. Such amethod wherein said positions are positions in the image at whichsubject movement has been detected. Such a method further comprising thestep of frequency filtering each of said plurality of signals to excludethose outside predetermined expected physiological range for said one ormore vital signs. Such a method further comprising the step ofcalculating the logarithm of said image intensity values to form alogarithm image and said step of analysing the image is performed onsaid logarithm image. Such a method wherein said first plurality ofsignal quality indices comprise measures of periodicity, for example oneor more of peak consistency and amplitude consistency. Such a methodwherein said step of multi-dimensional component analysis comprisesdecomposing the retained signals into their components, for example byone of principal component analysis or independent component analysis.Such a method wherein said second plurality of signal quality indicescomprise measures of one or more of: periodicity, spatial distributionwithin the image, uniformity and variability of the component. Such amethod further comprising the step of determining from said secondplurality of signal quality indices a confidence value for each of saidretained components and using said confidence value in said selectingstep. Such a method wherein said selecting step comprises weighting saidretained components by said confidence value and updating a priorestimate of said one or more vital signs by said weighted components.Such a method further comprising the step of down-weighting said priorestimate of said one or more vital signs by a predetermined amountbefore updating it with said weighted components. Such a method furthercomprising the step of detecting the amount of subject movement in theimage and varying said predetermined amount in dependence upon thedetected amount of subject movement. Such a method wherein the amount ofsubject movement is detected by determining the amount of variation inimage intensity with time at each of said respective plurality ofpositions in the image. Such a method wherein said step of determiningfrom said second plurality of signal quality indices a confidence valuefor each of said retained components is found using a machine learningtechnique.

Another embodiment, which may be used alone or with either or both ofthe embodiments above, provides a method of monitoring a human or animalsubject comprising the steps of: capturing a video image of the subjectconsisting of a time series of image frames each frame comprising apixel array of image intensity values; analysing the video image todetect signals comprising temporal variations in the image intensityrepresentative of movement of the subject; and outputting an indicationof the movement of the subject; wherein the step of analysing the videoimage to detect signals comprising temporal variations in the imageintensity representative of movement of the subject comprises: measuringthe variation with time of the image intensity at each of a plurality ofpositions in the image to form a respective plurality of movementsignals; grouping the movement signals into a plurality of groupsaccording to their position in the image, quantifying the variability ineach of the movement signals and forming for each group a representativesingle movement signal, determining whether the variability of therepresentative movement signal is above a predetermined threshold, anddetermining movement as being present at that position in the image ifthe variability of the retained movement signal is above thepredetermined threshold.

The methods above may have one or more of the following optionalfeatures: Such a method wherein the step of measuring the variation withtime of the image intensity at each of a plurality of positions in theimage comprises detecting spatial intensity variations representingedges in the image to form an edge image; and measuring the variationwith time of the edge image at each of a plurality of positions in theimage to form the respective plurality of movement signals. Such amethod wherein the step of detecting spatial intensity variationsrepresenting edges in the image to form an edge image comprises applyinga kernel convolution to each frame of the image, the kernel convolutioncombining the intensities of a plurality of neighbouring pixels in theframe to detect edges in the image. Such a method wherein a function isapplied to the image intensities that magnifies lower intensities andreduces the prominence of greater intensities to produce anon-linearly-scaled image, and the step of measuring the amount ofvariation in image intensity with time is conducted upon on theresultant image. Such a method comprising the step of determining thelogarithm of the image intensities in each frame to form a logarithmimage and wherein said step of analysing is conducted upon the logarithmimage. Such a method wherein the step of forming for each group arepresentative single movement signal comprises retaining for each groupa predetermined ordinal one of the movement signals ordered by theirvariability, determining whether the variability of the retainedmovement signals are above a predetermined threshold, and determiningmovement as being present at that position in the image if thevariability of the retained movement signal is above the predeterminedthreshold. Such a method wherein each of said plurality of positionscomprises at least one pixel of the image. Such a method wherein theintensity values of a plurality of neighbouring pixels are combinedtogether to form a spatially resized image upon which said analysis stepis conducted. Such a method wherein a temporally resized image uponwhich said analysis step is conducted is formed by combining togethercorresponding pixel values in a plurality of successive frames of thevideo image. Such a method wherein predetermined areas of said videoimage are masked out. Such a method further comprising the step ofdetecting global intensity variations in the image and de-correlatingthem from the derivative image. Such a method wherein the step ofdetecting global intensity variations comprises detecting variations inimage intensity in predefined areas of the image. Such a method whereinthe step of detecting global intensity variations comprises detectingprincipal components in the variations in image intensity, and retainingas representative of global intensity variations only those principalcomponents whose amplitude variability is above a predeterminedthreshold. Such a method further comprising the step of compensating theimage for pixel noise by down-weighting signals having low imageintensity.

The invention may be embodied as a system for monitoring a human oranimal subject in accordance with the method above, comprising: a videocamera adapted to capture a video image of the subject; a display; avideo image processing unit adapted to process the image in accordancewith the methods above.

1. A method of monitoring a human or animal subject comprising the stepsof: capturing a video image of the subject consisting of a time seriesof image frames each frame comprising a pixel array of image intensityvalues; analysing the video image to detect signals comprising temporalvariations in the image intensity representative of movement of thesubject; and outputting an indication of the movement of the subject;wherein the step of analysing the video image to detect signalscomprising temporal variations in the image intensity representative ofmovement of the subject comprises: measuring the variation with time ofthe image intensity at each of a plurality of positions in the image toform a respective plurality of movement signals; grouping the movementsignals into a plurality of groups according to their position in theimage, quantifying the variability in each of the movement signals andforming for each group a representative single movement signal,determining whether the variability of the representative movementsignal is above a predetermined threshold, and determining movement asbeing present at that position in the image if the variability of theretained movement signal is above the predetermined threshold.
 2. Amethod according to claim 1 wherein the step of measuring the variationwith time of the image intensity at each of a plurality of positions inthe image comprises detecting spatial intensity variations representingedges in the image to form an edge image; and measuring the variationwith time of the edge image at each of a plurality of positions in theimage to form the respective plurality of movement signals.
 3. A methodaccording to claim 2 wherein the step of detecting spatial intensityvariations representing edges in the image to form an edge imagecomprises applying a kernel convolution to each frame of the image, thekernel convolution combining the intensities of a plurality ofneighbouring pixels in the frame to detect edges in the image.
 4. Amethod according to claim 1 wherein a function is applied to the imageintensities that magnifies lower intensities and reduces the prominenceof greater intensities to produce a non-linearly-scaled image, and thestep of measuring the amount of variation in image intensity with timeis conducted upon on the resultant image.
 5. A method according to claim4 comprising the step of determining the logarithm of the imageintensities in each frame to form a logarithm image and wherein saidstep of analysing is conducted upon the logarithm image.
 6. A methodaccording to claim 1 wherein the step of forming for each group arepresentative single movement signal comprises retaining for each groupa predetermined ordinal one of the movement signals ordered by theirvariability, determining whether the variability of the retainedmovement signals are above a predetermined threshold, and determiningmovement as being present at that position in the image if thevariability of the retained movement signal is above the predeterminedthreshold.
 7. A method according to claim 1, wherein each of saidplurality of positions comprises at least one pixel of the image.
 8. Amethod according to claim 1 wherein the intensity values of a pluralityof neighbouring pixels are combined together to form a spatially resizedimage upon which said analysis step is conducted.
 9. A method accordingto claim 1 wherein a temporally resized image upon which said analysisstep is conducted is formed by combining together corresponding pixelvalues in a plurality of successive frames of the video image.
 10. Amethod according to claim 1 wherein predetermined areas of said videoimage are masked out.
 11. A method according to claim 1 furthercomprising the step of detecting global intensity variations in theimage and de-correlating them from the image.
 12. A method according toclaim 11 wherein the step of detecting global intensity variationscomprises detecting variations in image intensity in predefined areas ofthe image.
 13. A method according to claim 11 wherein the step ofdetecting global intensity variations comprises detecting principalcomponents in the variations in image intensity, and retaining asrepresentative of global intensity variations only those principalcomponents whose amplitude variability is above a predeterminedthreshold.
 14. A method according to claim 1 further comprising the stepof compensating the image for pixel noise by re-weighting signalsaccording to their average intensity.
 15. A system for monitoring ahuman or animal subject in accordance with the method of claim 1,comprising: a video camera adapted to capture a video image of thesubject; a display; a video image processing unit adapted to process theimage in accordance with the method of any one of the preceding claims.